Unpack
Description
The Unpack function unpacks data from a single packed column into
multiple columns. The packed column is composed of multiple virtual
columns, which become the output columns. To determine the virtual
columns, the function must have either the delimiter that separates
them in the packed column or their lengths.
Note: This function is only available when tdplyr is connected to Vantage 1.1
or later versions.
Usage
td_unpack_sqle (
data = NULL,
input.column = NULL,
output.columns = NULL,
output.datatypes = NULL,
delimiter = ",",
column.length = NULL,
regex = "(.*)",
regex.set = 1,
exception = FALSE,
data.order.column = NULL
)
Arguments
data |
Required Argument.
Specifies the tbl_teradata containing the input attributes.
|
data.order.column |
Optional Argument.
Specifies Order By columns for "data".
Values to this argument can be provided as a vector, if multiple
columns are used for ordering.
Types: character OR vector of Strings (character)
|
input.column |
Required Argument.
Specifies the name of the input column that contains the packed
data.
Types: character
|
output.columns |
Required Argument.
Specifies the names to give to the output columns, in the order in
which the corresponding virtual columns appear in "input.column".If you
specify fewer output column names than there are virtual input
columns, the function ignores the extra virtual input columns. That
is, if the packed data contains x+y virtual columns and the
"output.columns" argument specifies x output column names, the function
assigns the names to the first x virtual columns and ignores the
remaining y virtual columns.
Types:character OR vector of Strings (characters)
|
output.datatypes |
Required Argument.
Specifies the datatypes of the unpacked output columns. Supported
values for this argument are VARCHAR, integer, numeric, TIME, DATE, and
TIMESTAMP. If "output.datatypes" specifies only one value and
"output.columns" specifies multiple columns, then the specified value
applies to every "output.column". If "output.datatypes" specifies
multiple values, then it must specify a value for each "output.column".
The nth datatype corresponds to the nth "output.column".The function
can output only 16 VARCHAR columns.
Types:character OR vector of Strings (characters)
|
delimiter |
Optional Argument.
Specifies the delimiter (a string) that separates the virtual
columns in the packed data. The default delimiter is comma (,). If the
virtual columns are separated by a delimiter, then specify the
delimiter with this argument; otherwise, specify the "column.length"
argument. Do not specify both this argument and the "column.length"
argument.
Default Value: ","
Types: character
|
column.length |
Optional Argument.
Specifies the lengths of the virtual columns; therefore, to use this
argument, you must know the length of each virtual column. If
"column.length" specifies only one value and "output.columns" specifies
multiple columns, then the specified value applies to every
output column. If "column.length" specifies multiple values, then it
must specify a value for each output column. The nth datatype
corresponds to the nth output column. However, the last "column.name"
can be an asterisk (*), which represents a single virtual column that
contains the remaining data. For example, if the first three virtual
columns have the lengths 2, 1, and 3, and all remaining data belongs
to the fourth virtual column, you can specify column.length ("2",
"1", "3", *). If you specify this argument, you must omit the
delimiter argument.
Types:character OR vector of Strings (characters)
|
regex |
Optional Argument.
Specifies a regular expression that describes a row of packed data,
enabling the function to find the data values. A row of packed data
contains one data value for each virtual column, but the row might
also contain other information (such as the virtual column name). In
this argument "regex", each data value is enclosed in parentheses.
For example, suppose that the packed data has two virtual columns,
age and sex, and that one row of packed data is: age:34,sex:male, the
"regex" that describes the row is ".*:(.*)". The ".*:"
matches the virtual column names, age and sex, and the "(.*)" matches
the values, 34 and male. The default "regex" is "(.*)"
which matches the whole string (between delimiters, if any). When
applied to the preceding sample row, the default "regex"
causes the function to return "age:34" and "sex:male" as data values.
To represent multiple data groups in "regex", use multiple
pairs of parentheses. By default, the last data group in
"regex" represents the data value (other data groups are
assumed to be virtual column names or unwanted data). If a different
data group represents the data value, specify its group number with
the "regex.set" argument.
Default Value: "(.*)"
Types: character
|
regex.set |
Optional Argument.
Specifies the ordinal number of the data group in "regex"
that represents the data value in a virtual column. By default, the
last data group in "regex" represents the data value. For
example, suppose that "regex" is: "([a-zA-Z]*):(.*)" If
group_number is "1", then "([a-zA-Z]*)" represents the data value. If
group_number is "2", then "(.*)" represents the data value.
Default Value: 1
Types: integer
|
exception |
Optional Argument.
Specifies whether the function ignores rows that contain invalid
data; that is, it continues without outputting them, which causes the
function to fail if it encounters a row with invalid data.
Default Value: FALSE
Types: logical
|
Value
Function returns an object of class "td_unpack_sqle" which is a named
list containing object of class "tbl_teradata".
Named list member can be referenced directly with the "$" operator
using the name: result.
Examples
# Get the current context/connection.
con <- td_get_context()$connection
# Load example data.
loadExampleData("unpack_example", "ville_tempdata", "ville_tempdata1")
# Create object(s) of class "tbl_teradata".
ville_tempdata <- tbl(con, "ville_tempdata")
ville_tempdata1 <- tbl(con, "ville_tempdata1")
# Example 1 - Using the delimiter argument.
td_unpack_out1 <- td_unpack_sqle(data = ville_tempdata,
input.column = "packed_temp_data",
output.columns = c("city","state","temp_F"),
output.datatypes = c("varchar","varchar","real"),
delimiter = ",",
regex = '(.*)',
regex.set = 1,
exception = TRUE
)
# Example 2 - Using column.length argument
td_unpack_out2 <- td_unpack_sqle(data = ville_tempdata1,
input.column = "packed_temp_data",
output.columns = c("city","state","temp_F"),
output.datatypes = c("varchar","varchar","real"),
column.length = c("9","9","4"),
regex = '(.*)',
regex.set = 1,
exception = TRUE
)